Design and Experimental Evaluation of a Hierarchical Controller for an Autonomous Ground Vehicle With Large Uncertainties

Autonomous ground vehicles (AGVs) are receiving increasing attention, and the motion planning control problem for these has become a hot research topic. In real applications such as material handling, an AGV is subject to large uncertainties its challenging. this article, we investigate by proposing hierarchical scheme, which integrated model predictive (MPC)-based path trajectory tracking at high level, reduced-order extended state observer (RESO)-based dynamic low level. The level consists of MPC-based improved planner, velocity controller. Both problems formulated under MPC framework. employs idea active disturbance rejection (ADRC). estimated via RESO then compensated in time. We show that, first-order uncertain model, RESO-based only needs know direction. Finally, simulations experiments on with different payloads conducted. results illustrate that proposed scheme achieves satisfactory performance uncertainties.